The Effective Statistician - in association with PSI
Linear Mixed Models - A Refresher And Introduction
May 6, 2024
This podcast explores the importance and evolution of linear mixed models, emphasizing their role in statistical analysis. It discusses modeling repeated measurements with random effects, sample size influences, Bayesian models, and handling missing values. Additionally, it covers modeling assumptions, software preferences, and resources for effective statistical modeling, highlighting the significance of rational assumptions and the preference for using SAS in modeling.
25:29
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Quick takeaways
- Linearity in coefficients is crucial in understanding linear mixed models, focusing on beta rather than axes.
- Covariance structures and random effects account for non-independence in patient-specific effects within linear mixed models.
Deep dives
Overview of Linear Mixed Models
Linear mixed models involve the incorporation of random effects models, serving as a standard starting point for many in understanding modeling. The linearity in the coefficients, such as Y equals beta times X, is crucial to grasp, highlighting the focus on the linearity in the beta rather than in the axes. Understanding the historical development of linear mixed effects models delves into their origins in the early 80s with refinement and expansion over time, despite still seeming relatively new to many users.
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